Deep networks in online malware detection

Abstrakt

Deep learning is usually applied to static datasets. If used for classification based on data streams, it is not easy to take into account a non-stationarity. This paper presents work in progress on a new method for online deep classification learning in data streams with slow or moderate drift, highly relevant for the application domain of malware detection. The method uses a combination of multilayer perceptron and variational autoencoder to achieve constant memory consumption by encoding past data to a generative model. This can make online learning of neural networks more accessible for independent adaptive systems with limited memory. First results for real-world malware stream data are presented.